Semantic closure—the self-referential mechanism by which symbols construct and interpret their own contexts—arises evolutionarily from simple reaction networks to self-constructing chemical systems with anticipatory capabilities. By extending relational biology models with temporal parameters, the work identifies self-reference as necessary for robust self-replication and open-ended evolution. A computational enactivist framework integrates autopoiesis, anticipation, and adaptation to address the problem of relevance realization, providing a theoretical basis for naturalizing agency and cognition.
Agency emerges gradually from material organization through a hierarchy of temporal structures. By incorporating time into the analysis of self-referential biological systems, the paper distinguishes autonomy (precarious closure to efficient causation), goal-directedness (maintaining viability-supporting organization), agency (endogenous anticipatory structure modulating organism-environment coupling), and open-endedness (reconstructing future possibilities). The framework uses Asynchronous Dynamic Bayesian Networks to model history-dependent, revisable dependencies. It reconciles Rosennean anticipation with organizational closure, treats Markov blankets and active inference as derived redescriptions rather than first principles, and reinterprets computational enactivism. The hierarchy spans from proto-agential chemical systems to fully semantically closed agents, with implications for multicellular organisms, synthetic life, and neuroscience.
The extended mind hypothesis proposes that cognitive processes can extend beyond the brain into the body and environment, but critics argue that coupling to external elements does not make them part of cognition—a charge called the coupling-constitution fallacy. Using an evolutionary robotics approach with a minimal model of two acoustically coupled agents, the authors demonstrate that the interaction as a whole has properties not reducible to the isolated agents. They also show that the coupled agents' neural dynamics exhibit formal properties impossible for those networks in isolation. The findings suggest the fallacy stems from misunderstanding how nonlinear interactions constitute cognitive systems in dynamical systems theory.